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# LeViT

## Overview

The LeViT model was proposed in [LeViT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the [Vision Transformer (ViT)](vit) in performance and efficiency by a few architectural differences such as activation maps with decreasing resolutions in Transformers and the introduction of an attention bias to integrate positional information.

The abstract from the paper is the following:

*We design a family of image classification architectures that optimize the trade-off between accuracy
and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures,
which are competitive on highly parallel processing hardware. We revisit principles from the extensive
literature on convolutional neural networks to apply them to transformers, in particular activation maps
with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information
in vision transformers. As a result, we propose LeVIT: a hybrid neural network for fast inference image classification.
We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of
application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable
to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect
to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. *

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/levit_architecture.png"
alt="drawing" width="600"/>

<small> LeViT Architecture. Taken from the <a href="https://arxiv.org/abs/2104.01136">original paper</a>.</small>

Tips:

- Compared to ViT, LeViT models use an additional distillation head to effectively learn from a teacher (which, in the LeViT paper, is a ResNet like-model). The distillation head is learned through backpropagation under supervision of a ResNet like-model. They also draw inspiration from convolution neural networks to use activation maps with decreasing resolutions to increase the efficiency.
- There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
  of the final hidden state and not using the distillation head, or (2) by placing both a prediction head and distillation
  head on top of the final hidden state. In that case, the prediction head is trained using regular cross-entropy between
  the prediction of the head and the ground-truth label, while the distillation prediction head is trained using hard distillation
  (cross-entropy between the prediction of the distillation head and the label predicted by the teacher). At inference time,
  one takes the average prediction between both heads as final prediction. (2) is also called "fine-tuning with distillation",
  because one relies on a teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds
  to [`LevitForImageClassification`] and (2) corresponds to [`LevitForImageClassificationWithTeacher`].
- All released checkpoints were pre-trained and fine-tuned on  [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)
  (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). only. No external data was used. This is in
  contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
  pre-training.
- The authors of LeViT released 5 trained LeViT models, which you can directly plug into [`LevitModel`] or [`LevitForImageClassification`].
  Techniques like data augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
  (while only using ImageNet-1k for pre-training). The 5 variants available are (all trained on images of size 224x224):
  *facebook/levit-128S*, *facebook/levit-128*, *facebook/levit-192*, *facebook/levit-256* and
  *facebook/levit-384*. Note that one should use [`LevitImageProcessor`] in order to
  prepare images for the model.
- [`LevitForImageClassificationWithTeacher`] currently supports only inference and not training or fine-tuning.
- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer)
  (you can just replace [`ViTFeatureExtractor`] by [`LevitImageProcessor`] and [`ViTForImageClassification`] by [`LevitForImageClassification`] or [`LevitForImageClassificationWithTeacher`]).

This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/facebookresearch/LeViT).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LeViT.

<PipelineTag pipeline="image-classification"/>

- [`LevitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## LevitConfig

[[autodoc]] LevitConfig

## LevitFeatureExtractor

[[autodoc]] LevitFeatureExtractor
    - __call__

## LevitImageProcessor

  [[autodoc]] LevitImageProcessor
    - preprocess


## LevitModel

[[autodoc]] LevitModel
    - forward

## LevitForImageClassification

[[autodoc]] LevitForImageClassification
    - forward

## LevitForImageClassificationWithTeacher

[[autodoc]] LevitForImageClassificationWithTeacher
    - forward
